trait HasClassifierActivationProperties extends ParamsAndFeaturesWritable
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- val activation: Param[String]
Whether to enable caching DataFrames or RDDs during the training (Default depends on model).
- final def asInstanceOf[T0]: T0
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- final def clear(param: Param[_]): HasClassifierActivationProperties.this.type
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- @throws(classOf[java.lang.CloneNotSupportedException]) @HotSpotIntrinsicCandidate() @native()
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- def explainParam(param: Param[_]): String
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- def explainParams(): String
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- final def extractParamMap(): ParamMap
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- final def extractParamMap(extra: ParamMap): ParamMap
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- val features: ArrayBuffer[Feature[_, _, _]]
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- def get[T](feature: StructFeature[T]): Option[T]
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- def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
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- def get[T](feature: SetFeature[T]): Option[Set[T]]
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- final def get[T](param: Param[T]): Option[T]
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- def getActivation: String
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- final def isSet(param: Param[_]): Boolean
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- val multilabel: BooleanParam
Whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0).
Whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0). Default is False i.e. multi-class
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- def onWrite(path: String, spark: SparkSession): Unit
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- @throws("If the input path already exists but overwrite is not enabled.") @Since("1.6.0")
- def set[T](feature: StructFeature[T], value: T): HasClassifierActivationProperties.this.type
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- def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): HasClassifierActivationProperties.this.type
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- def set[T](feature: SetFeature[T], value: Set[T]): HasClassifierActivationProperties.this.type
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- def set[T](feature: ArrayFeature[T], value: Array[T]): HasClassifierActivationProperties.this.type
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- final def set(paramPair: ParamPair[_]): HasClassifierActivationProperties.this.type
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- final def set(param: String, value: Any): HasClassifierActivationProperties.this.type
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- final def set[T](param: Param[T], value: T): HasClassifierActivationProperties.this.type
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- def setActivation(value: String): HasClassifierActivationProperties.this.type
- def setDefault[T](feature: StructFeature[T], value: () => T): HasClassifierActivationProperties.this.type
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- def setDefault[K, V](feature: MapFeature[K, V], value: () => Map[K, V]): HasClassifierActivationProperties.this.type
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- def setDefault[T](feature: SetFeature[T], value: () => Set[T]): HasClassifierActivationProperties.this.type
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- def setDefault[T](feature: ArrayFeature[T], value: () => Array[T]): HasClassifierActivationProperties.this.type
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- final def setDefault(paramPairs: ParamPair[_]*): HasClassifierActivationProperties.this.type
- Attributes
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- final def setDefault[T](param: Param[T], value: T): HasClassifierActivationProperties.this.type
- Attributes
- protected[org.apache.spark.ml]
- Definition Classes
- Params
- def setMultilabel(value: Boolean): HasClassifierActivationProperties.this.type
Set whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0).
Set whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0). Default is False i.e. multi-class
- def setThreshold(threshold: Float): HasClassifierActivationProperties.this.type
Choose the threshold to determine which logits are considered to be positive or negative.
Choose the threshold to determine which logits are considered to be positive or negative. (Default:
0.5f). The value should be between 0.0 and 1.0. Changing the threshold value will affect the resulting labels and can be used to adjust the balance between precision and recall in the classification process. - final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- val threshold: FloatParam
Choose the threshold to determine which logits are considered to be positive or negative.
Choose the threshold to determine which logits are considered to be positive or negative. (Default:
0.5f). The value should be between 0.0 and 1.0. Changing the threshold value will affect the resulting labels and can be used to adjust the balance between precision and recall in the classification process. - def toString(): String
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- final def wait(arg0: Long, arg1: Int): Unit
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- def write: MLWriter
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- ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
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- Deprecated
(Since version 9)